Radio Transmission Technology with Evolution and Self-Learning Algorithms (RTT-ESLA)
in conjunction with IEEE PIRMC 2017 conference in Montreal, Canada, in Oct. 8/13, 2017
Home Program Committee5GCHFB

With the developments and applications of wireless communications, more and more applications require advanced radio transmission technology (RTT) to reach the goal of low-power, high spectrum efficiency and flexible to multiple scenarios such as mobile broadband, ultra reliable communications, internet-of-things. Recently intelligent optimization and self-learning algorithms are widely studied. Evolution algorithm is to find maximum point with complex non-continuous cost functions by biologic technology such as genetic algorithm and particle swarm optimization. Self-learning algorithm is lighted up with the success of machine-learning in artificial intelligent field.  With the strong requirements to RTT and fruitful achievements in evolution and self-learning algorithm (ESLA), it is foreseen that applying ESLA to RTT may help solve some challenges in wireless communications.

This workshop will provide a forum for both industry and academia to exchange views and visions. Topics of interest include but are not limited to the following:

•  Overview of intelligent optimization
•  Overview of machine-learning algorithms
•  Massive MIMO with ESLA
•  Position/location estimation with ESLA
•  Radio resource management with ESLA
•  Signal detection with ESLA
•  Channel estimation and tracking with ESLA
•  Channel coding and decoding with ESLA
•  Power control with ESLA

Important Dates

Paper Submission:
Acceptance Notification:

July 27, 2017
August 11, 2017
August 18, 2017
October 10, 2017

Submission Entrance: